Chapter04.KNN

파이썬을 활용한 이커머스 데이터분석_강의를 듣고 따라했던 코딩과 요점을 정리하였다.

출처: fast campus

Chapter04. 고객 이탈 예측 (KNN)¶

분석의 목적¶

KNN 알고리즘으로 고객 이탈(Customer Churn)을 예측

Binary Classification : 'Yer' or 'No' 를 예측

이번에는 이동통신사 데이터를 다룰 것이다.

In [105]:

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns

In [106]:

data = pd.read_csv('./data/churn.csv')

In [107]:

data

Out[107]:

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7590-VHVEG	Female	0	Yes	No	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	5575-GNVDE	Male	0	No	No	34	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.50	No
2	3668-QPYBK	Male	0	No	No	2	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	7795-CFOCW	Male	0	No	No	45	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	9237-HQITU	Female	0	No	No	2	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	6840-RESVB	Male	0	Yes	Yes	24	Yes	Yes	DSL	Yes	No	Yes	Yes	Yes	Yes	One year	Yes	Mailed check	84.80	1990.50	No
7039	2234-XADUH	Female	0	Yes	Yes	72	Yes	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes	One year	Yes	Credit card (automatic)	103.20	7362.90	No
7040	4801-JZAZL	Female	0	Yes	Yes	11	No	No phone service	DSL	Yes	No	No	No	No	No	Month-to-month	Yes	Electronic check	29.60	346.45	No
7041	8361-LTMKD	Male	1	Yes	No	4	Yes	Yes	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Mailed check	74.40	306.60	Yes
7042	3186-AJIEK	Male	0	No	No	66	Yes	No	Fiber optic	Yes	No	Yes	Yes	Yes	Yes	Two year	Yes	Bank transfer (automatic)	105.65	6844.50	No

7043 rows × 21 columns

In [108]:

data.head()

Out[108]:

	customerID	gender	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7590-VHVEG	Female	Yes	No	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	5575-GNVDE	Male	No	No	34	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.50	No
2	3668-QPYBK	Male	No	No	2	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	7795-CFOCW	Male	No	No	45	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	9237-HQITU	Female	No	No	2	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes

글자수가 길어서 컬럼중에 'OnlineSecurity...'으로 표시가 되었다. 판다스에서는 이것을 해결해주는 기능이 있다. 다음의 코드를 실행해 보자.

In [109]:

pd.set_option('display.max_columns', 30)

In [110]:

data.head()

Out[110]:

	customerID	gender	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7590-VHVEG	Female	Yes	No	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	5575-GNVDE	Male	No	No	34	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.50	No
2	3668-QPYBK	Male	No	No	2	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	7795-CFOCW	Male	No	No	45	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	9237-HQITU	Female	No	No	2	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes

이번에는 열 100줄을 생략없이 보고싶을때 할 수 있는 방법이다.

In [111]:

data.head(100)

Out[111]:

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7590-VHVEG	Female	0	Yes	No	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	5575-GNVDE	Male	0	No	No	34	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.50	No
2	3668-QPYBK	Male	0	No	No	2	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	7795-CFOCW	Male	0	No	No	45	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	9237-HQITU	Female	0	No	No	2	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes
5	9305-CDSKC	Female	0	No	No	8	Yes	Yes	Fiber optic	No	No	Yes	No	Yes	Yes	Month-to-month	Yes	Electronic check	99.65	820.50	Yes
6	1452-KIOVK	Male	0	No	Yes	22	Yes	Yes	Fiber optic	No	Yes	No	No	Yes	No	Month-to-month	Yes	Credit card (automatic)	89.10	1949.40	No
7	6713-OKOMC	Female	0	No	No	10	No	No phone service	DSL	Yes	No	No	No	No	No	Month-to-month	No	Mailed check	29.75	301.90	No
8	7892-POOKP	Female	0	Yes	No	28	Yes	Yes	Fiber optic	No	No	Yes	Yes	Yes	Yes	Month-to-month	Yes	Electronic check	104.80	3046.05	Yes
9	6388-TABGU	Male	0	No	Yes	62	Yes	No	DSL	Yes	Yes	No	No	No	No	One year	No	Bank transfer (automatic)	56.15	3487.95	No
10	9763-GRSKD	Male	0	Yes	Yes	13	Yes	No	DSL	Yes	No	No	No	No	No	Month-to-month	Yes	Mailed check	49.95	587.45	No
11	7469-LKBCI	Male	0	No	No	16	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	No	Credit card (automatic)	18.95	326.80	No
12	8091-TTVAX	Male	0	Yes	No	58	Yes	Yes	Fiber optic	No	No	Yes	No	Yes	Yes	One year	No	Credit card (automatic)	100.35	5681.10	No
13	0280-XJGEX	Male	0	No	No	49	Yes	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes	Month-to-month	Yes	Bank transfer (automatic)	103.70	5036.30	Yes
14	5129-JLPIS	Male	0	No	No	25	Yes	No	Fiber optic	Yes	No	Yes	Yes	Yes	Yes	Month-to-month	Yes	Electronic check	105.50	2686.05	No
15	3655-SNQYZ	Female	0	Yes	Yes	69	Yes	Yes	Fiber optic	Yes	Yes	Yes	Yes	Yes	Yes	Two year	No	Credit card (automatic)	113.25	7895.15	No
16	8191-XWSZG	Female	0	No	No	52	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	One year	No	Mailed check	20.65	1022.95	No
17	9959-WOFKT	Male	0	No	Yes	71	Yes	Yes	Fiber optic	Yes	No	Yes	No	Yes	Yes	Two year	No	Bank transfer (automatic)	106.70	7382.25	No
18	4190-MFLUW	Female	0	Yes	Yes	10	Yes	No	DSL	No	No	Yes	Yes	No	No	Month-to-month	No	Credit card (automatic)	55.20	528.35	Yes
19	4183-MYFRB	Female	0	No	No	21	Yes	No	Fiber optic	No	Yes	Yes	No	No	Yes	Month-to-month	Yes	Electronic check	90.05	1862.90	No
20	8779-QRDMV	Male	1	No	No	1	No	No phone service	DSL	No	No	Yes	No	No	Yes	Month-to-month	Yes	Electronic check	39.65	39.65	Yes
21	1680-VDCWW	Male	0	Yes	No	12	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	One year	No	Bank transfer (automatic)	19.80	202.25	No
22	1066-JKSGK	Male	0	No	No	1	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Month-to-month	No	Mailed check	20.15	20.15	Yes
23	3638-WEABW	Female	0	Yes	No	58	Yes	Yes	DSL	No	Yes	No	Yes	No	No	Two year	Yes	Credit card (automatic)	59.90	3505.10	No
24	6322-HRPFA	Male	0	Yes	Yes	49	Yes	No	DSL	Yes	Yes	No	Yes	No	No	Month-to-month	No	Credit card (automatic)	59.60	2970.30	No
25	6865-JZNKO	Female	0	No	No	30	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Bank transfer (automatic)	55.30	1530.60	No
26	6467-CHFZW	Male	0	Yes	Yes	47	Yes	Yes	Fiber optic	No	Yes	No	No	Yes	Yes	Month-to-month	Yes	Electronic check	99.35	4749.15	Yes
27	8665-UTDHZ	Male	0	Yes	Yes	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	No	Electronic check	30.20	30.20	Yes
28	5248-YGIJN	Male	0	Yes	No	72	Yes	Yes	DSL	Yes	Yes	Yes	Yes	Yes	Yes	Two year	Yes	Credit card (automatic)	90.25	6369.45	No
29	8773-HHUOZ	Female	0	No	Yes	17	Yes	No	DSL	No	No	No	No	Yes	Yes	Month-to-month	Yes	Mailed check	64.70	1093.10	Yes
30	3841-NFECX	Female	1	Yes	No	71	Yes	Yes	Fiber optic	Yes	Yes	Yes	Yes	No	No	Two year	Yes	Credit card (automatic)	96.35	6766.95	No
31	4929-XIHVW	Male	1	Yes	No	2	Yes	No	Fiber optic	No	No	Yes	No	Yes	Yes	Month-to-month	Yes	Credit card (automatic)	95.50	181.65	No
32	6827-IEAUQ	Female	0	Yes	Yes	27	Yes	No	DSL	Yes	Yes	Yes	Yes	No	No	One year	No	Mailed check	66.15	1874.45	No
33	7310-EGVHZ	Male	0	No	No	1	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Month-to-month	No	Bank transfer (automatic)	20.20	20.20	No
34	3413-BMNZE	Male	1	No	No	1	Yes	No	DSL	No	No	No	No	No	No	Month-to-month	No	Bank transfer (automatic)	45.25	45.25	No
35	6234-RAAPL	Female	0	Yes	Yes	72	Yes	Yes	Fiber optic	Yes	Yes	No	Yes	Yes	No	Two year	No	Bank transfer (automatic)	99.90	7251.70	No
36	6047-YHPVI	Male	0	No	No	5	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	69.70	316.90	Yes
37	6572-ADKRS	Female	0	No	No	46	Yes	No	Fiber optic	No	No	Yes	No	No	No	Month-to-month	Yes	Credit card (automatic)	74.80	3548.30	No
38	5380-WJKOV	Male	0	No	No	34	Yes	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes	Month-to-month	Yes	Electronic check	106.35	3549.25	Yes
39	8168-UQWWF	Female	0	No	No	11	Yes	Yes	Fiber optic	No	No	Yes	No	Yes	Yes	Month-to-month	Yes	Bank transfer (automatic)	97.85	1105.40	Yes
40	8865-TNMNX	Male	0	Yes	Yes	10	Yes	No	DSL	No	Yes	No	No	No	No	One year	No	Mailed check	49.55	475.70	No
41	9489-DEDVP	Female	0	Yes	Yes	70	Yes	Yes	DSL	Yes	Yes	No	No	Yes	No	Two year	Yes	Credit card (automatic)	69.20	4872.35	No
42	9867-JCZSP	Female	0	Yes	Yes	17	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	One year	No	Mailed check	20.75	418.25	No
43	4671-VJLCL	Female	0	No	No	63	Yes	Yes	DSL	Yes	Yes	Yes	Yes	Yes	No	Two year	Yes	Credit card (automatic)	79.85	4861.45	No
44	4080-IIARD	Female	0	Yes	No	13	Yes	Yes	DSL	Yes	Yes	No	Yes	Yes	No	Month-to-month	Yes	Electronic check	76.20	981.45	No
45	3714-NTNFO	Female	0	No	No	49	Yes	Yes	Fiber optic	No	No	No	No	No	Yes	Month-to-month	Yes	Electronic check	84.50	3906.70	No
46	5948-UJZLF	Male	0	No	No	2	Yes	No	DSL	No	Yes	No	No	No	No	Month-to-month	No	Mailed check	49.25	97.00	No
47	7760-OYPDY	Female	0	No	No	2	Yes	No	Fiber optic	No	No	No	No	Yes	No	Month-to-month	Yes	Electronic check	80.65	144.15	Yes
48	7639-LIAYI	Male	0	No	No	52	Yes	Yes	DSL	Yes	No	No	Yes	Yes	Yes	Two year	Yes	Credit card (automatic)	79.75	4217.80	No
49	2954-PIBKO	Female	0	Yes	Yes	69	Yes	Yes	DSL	Yes	No	Yes	Yes	No	No	Two year	Yes	Credit card (automatic)	64.15	4254.10	No
50	8012-SOUDQ	Female	1	No	No	43	Yes	Yes	Fiber optic	No	Yes	No	No	Yes	No	Month-to-month	Yes	Electronic check	90.25	3838.75	No
51	9420-LOJKX	Female	0	No	No	15	Yes	No	Fiber optic	Yes	Yes	No	No	Yes	Yes	Month-to-month	Yes	Credit card (automatic)	99.10	1426.40	Yes
52	6575-SUVOI	Female	1	Yes	No	25	Yes	Yes	DSL	Yes	No	No	Yes	Yes	No	Month-to-month	Yes	Credit card (automatic)	69.50	1752.65	No
53	7495-OOKFY	Female	1	Yes	No	8	Yes	Yes	Fiber optic	No	Yes	No	No	No	No	Month-to-month	Yes	Credit card (automatic)	80.65	633.30	Yes
54	4667-QONEA	Female	1	Yes	Yes	60	Yes	No	DSL	Yes	Yes	Yes	Yes	No	Yes	One year	Yes	Credit card (automatic)	74.85	4456.35	No
55	1658-BYGOY	Male	1	No	No	18	Yes	Yes	Fiber optic	No	No	No	No	Yes	Yes	Month-to-month	Yes	Electronic check	95.45	1752.55	Yes
56	8769-KKTPH	Female	0	Yes	Yes	63	Yes	Yes	Fiber optic	Yes	No	No	No	Yes	Yes	One year	Yes	Credit card (automatic)	99.65	6311.20	No
57	5067-XJQFU	Male	1	Yes	Yes	66	Yes	Yes	Fiber optic	No	Yes	Yes	Yes	Yes	Yes	One year	Yes	Electronic check	108.45	7076.35	No
58	3957-SQXML	Female	0	Yes	Yes	34	Yes	Yes	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	No	Credit card (automatic)	24.95	894.30	No
59	5954-BDFSG	Female	0	No	No	72	Yes	Yes	Fiber optic	No	No	Yes	Yes	Yes	Yes	Two year	Yes	Credit card (automatic)	107.50	7853.70	No
60	0434-CSFON	Female	0	Yes	No	47	Yes	Yes	Fiber optic	No	No	Yes	No	Yes	Yes	Month-to-month	Yes	Electronic check	100.50	4707.10	No
61	1215-FIGMP	Male	0	No	No	60	Yes	Yes	Fiber optic	No	Yes	No	No	Yes	No	Month-to-month	Yes	Bank transfer (automatic)	89.90	5450.70	No
62	0526-SXDJP	Male	0	Yes	No	72	No	No phone service	DSL	Yes	Yes	Yes	No	No	No	Two year	No	Bank transfer (automatic)	42.10	2962.00	No
63	0557-ASKVU	Female	0	Yes	Yes	18	Yes	No	DSL	No	No	Yes	Yes	No	No	One year	Yes	Credit card (automatic)	54.40	957.10	No
64	5698-BQJOH	Female	0	No	No	9	Yes	Yes	Fiber optic	No	No	No	No	Yes	Yes	Month-to-month	No	Electronic check	94.40	857.25	Yes
65	5122-CYFXA	Female	0	No	No	3	Yes	No	DSL	No	Yes	No	Yes	Yes	Yes	Month-to-month	Yes	Electronic check	75.30	244.10	No
66	8627-ZYGSZ	Male	0	Yes	No	47	Yes	Yes	Fiber optic	No	Yes	No	No	No	No	One year	Yes	Electronic check	78.90	3650.35	No
67	3410-YOQBQ	Female	0	No	No	31	Yes	No	DSL	No	Yes	Yes	Yes	Yes	Yes	Two year	No	Mailed check	79.20	2497.20	No
68	3170-NMYVV	Female	0	Yes	Yes	50	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	No	Bank transfer (automatic)	20.15	930.90	No
69	7410-OIEDU	Male	0	No	No	10	Yes	No	Fiber optic	Yes	No	Yes	No	No	No	Month-to-month	Yes	Mailed check	79.85	887.35	No
70	2273-QCKXA	Male	0	No	No	1	Yes	No	DSL	No	No	No	Yes	No	No	Month-to-month	No	Mailed check	49.05	49.05	No
71	0731-EBJQB	Female	0	Yes	Yes	52	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	One year	Yes	Electronic check	20.40	1090.65	No
72	1891-QRQSA	Male	1	Yes	Yes	64	Yes	Yes	Fiber optic	Yes	No	Yes	Yes	Yes	Yes	Two year	Yes	Bank transfer (automatic)	111.60	7099.00	No
73	8028-PNXHQ	Male	0	Yes	Yes	62	Yes	Yes	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	Yes	Bank transfer (automatic)	24.25	1424.60	No
74	5630-AHZIL	Female	0	No	Yes	3	Yes	No	DSL	Yes	No	No	Yes	No	Yes	Month-to-month	Yes	Bank transfer (automatic)	64.50	177.40	No
75	2673-CXQEU	Female	1	No	No	56	Yes	Yes	Fiber optic	Yes	Yes	Yes	No	Yes	Yes	One year	No	Electronic check	110.50	6139.50	No
76	6416-JNVRK	Female	0	No	No	46	Yes	No	DSL	No	No	No	No	No	Yes	One year	No	Credit card (automatic)	55.65	2688.85	No
77	5590-ZSKRV	Female	0	Yes	Yes	8	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	No	Mailed check	54.65	482.25	No
78	0191-ZHSKZ	Male	1	No	No	30	Yes	No	DSL	Yes	Yes	No	No	Yes	Yes	Month-to-month	Yes	Electronic check	74.75	2111.30	No
79	3887-PBQAO	Female	0	Yes	Yes	45	Yes	Yes	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	One year	Yes	Credit card (automatic)	25.90	1216.60	No
80	5919-TMRGD	Female	0	No	Yes	1	Yes	No	Fiber optic	No	No	No	No	Yes	No	Month-to-month	Yes	Electronic check	79.35	79.35	Yes
81	8108-UXRQN	Female	0	Yes	Yes	11	No	No phone service	DSL	Yes	No	No	No	Yes	Yes	Month-to-month	No	Electronic check	50.55	565.35	No
82	9191-MYQKX	Female	0	Yes	No	7	Yes	No	Fiber optic	No	No	Yes	No	No	No	Month-to-month	Yes	Bank transfer (automatic)	75.15	496.90	Yes
83	9919-YLNNG	Female	0	No	No	42	Yes	No	Fiber optic	No	Yes	Yes	Yes	Yes	Yes	Month-to-month	Yes	Bank transfer (automatic)	103.80	4327.50	No
84	0318-ZOPWS	Female	0	Yes	No	49	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	Yes	Bank transfer (automatic)	20.15	973.35	No
85	4445-ZJNMU	Male	0	No	No	9	Yes	Yes	Fiber optic	No	Yes	No	No	Yes	Yes	Month-to-month	Yes	Credit card (automatic)	99.30	918.75	No
86	4808-YNLEU	Female	0	Yes	No	35	Yes	No	DSL	Yes	No	No	No	Yes	No	One year	Yes	Bank transfer (automatic)	62.15	2215.45	No
87	1862-QRWPE	Female	0	Yes	Yes	48	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	No	Bank transfer (automatic)	20.65	1057.00	No
88	2796-NNUFI	Female	0	Yes	Yes	46	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	Yes	Mailed check	19.95	927.10	No
89	3016-KSVCP	Male	0	Yes	No	29	No	No phone service	DSL	No	No	No	No	Yes	No	Month-to-month	No	Mailed check	33.75	1009.25	No
90	4767-HZZHQ	Male	0	Yes	Yes	30	Yes	No	Fiber optic	No	Yes	Yes	No	No	No	Month-to-month	No	Bank transfer (automatic)	82.05	2570.20	No
91	2424-WVHPL	Male	1	No	No	1	Yes	No	Fiber optic	No	No	No	Yes	No	No	Month-to-month	No	Electronic check	74.70	74.70	No
92	7233-PAHHL	Male	0	Yes	Yes	66	Yes	Yes	DSL	Yes	No	Yes	Yes	Yes	Yes	Two year	Yes	Mailed check	84.00	5714.25	No
93	6067-NGCEU	Female	0	No	No	65	Yes	Yes	Fiber optic	Yes	Yes	Yes	No	Yes	Yes	Month-to-month	Yes	Credit card (automatic)	111.05	7107.00	No
94	9848-JQJTX	Male	0	No	No	72	Yes	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes	Two year	Yes	Bank transfer (automatic)	100.90	7459.05	No
95	8637-XJIVR	Female	0	No	No	12	Yes	Yes	Fiber optic	Yes	No	No	No	No	No	Month-to-month	Yes	Electronic check	78.95	927.35	Yes
96	9803-FTJCG	Male	0	Yes	Yes	71	Yes	Yes	DSL	Yes	Yes	No	Yes	No	No	One year	Yes	Credit card (automatic)	66.85	4748.70	No
97	0278-YXOOG	Male	0	No	No	5	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Month-to-month	No	Mailed check	21.05	113.85	Yes
98	3212-KXOCR	Male	0	No	No	52	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	No	Bank transfer (automatic)	21.00	1107.20	No
99	4598-XLKNJ	Female	1	Yes	No	25	Yes	No	Fiber optic	No	Yes	Yes	No	Yes	Yes	Month-to-month	Yes	Electronic check	98.50	2514.50	Yes

5 부터 ~ 94 까지 '...'으로 생략되어 있다. 이것을 보고싶을때도 위에서 했던거와 방법론적으로는 같다. row 로만 교체하면 된다.

In [112]:

pd.set_option('display.max_row', 100)

In [113]:

data.head(100)

Out[113]:

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7590-VHVEG	Female	0	Yes	No	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	5575-GNVDE	Male	0	No	No	34	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.50	No
2	3668-QPYBK	Male	0	No	No	2	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	7795-CFOCW	Male	0	No	No	45	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	9237-HQITU	Female	0	No	No	2	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes
5	9305-CDSKC	Female	0	No	No	8	Yes	Yes	Fiber optic	No	No	Yes	No	Yes	Yes	Month-to-month	Yes	Electronic check	99.65	820.50	Yes
6	1452-KIOVK	Male	0	No	Yes	22	Yes	Yes	Fiber optic	No	Yes	No	No	Yes	No	Month-to-month	Yes	Credit card (automatic)	89.10	1949.40	No
7	6713-OKOMC	Female	0	No	No	10	No	No phone service	DSL	Yes	No	No	No	No	No	Month-to-month	No	Mailed check	29.75	301.90	No
8	7892-POOKP	Female	0	Yes	No	28	Yes	Yes	Fiber optic	No	No	Yes	Yes	Yes	Yes	Month-to-month	Yes	Electronic check	104.80	3046.05	Yes
9	6388-TABGU	Male	0	No	Yes	62	Yes	No	DSL	Yes	Yes	No	No	No	No	One year	No	Bank transfer (automatic)	56.15	3487.95	No
10	9763-GRSKD	Male	0	Yes	Yes	13	Yes	No	DSL	Yes	No	No	No	No	No	Month-to-month	Yes	Mailed check	49.95	587.45	No
11	7469-LKBCI	Male	0	No	No	16	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	No	Credit card (automatic)	18.95	326.80	No
12	8091-TTVAX	Male	0	Yes	No	58	Yes	Yes	Fiber optic	No	No	Yes	No	Yes	Yes	One year	No	Credit card (automatic)	100.35	5681.10	No
13	0280-XJGEX	Male	0	No	No	49	Yes	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes	Month-to-month	Yes	Bank transfer (automatic)	103.70	5036.30	Yes
14	5129-JLPIS	Male	0	No	No	25	Yes	No	Fiber optic	Yes	No	Yes	Yes	Yes	Yes	Month-to-month	Yes	Electronic check	105.50	2686.05	No
15	3655-SNQYZ	Female	0	Yes	Yes	69	Yes	Yes	Fiber optic	Yes	Yes	Yes	Yes	Yes	Yes	Two year	No	Credit card (automatic)	113.25	7895.15	No
16	8191-XWSZG	Female	0	No	No	52	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	One year	No	Mailed check	20.65	1022.95	No
17	9959-WOFKT	Male	0	No	Yes	71	Yes	Yes	Fiber optic	Yes	No	Yes	No	Yes	Yes	Two year	No	Bank transfer (automatic)	106.70	7382.25	No
18	4190-MFLUW	Female	0	Yes	Yes	10	Yes	No	DSL	No	No	Yes	Yes	No	No	Month-to-month	No	Credit card (automatic)	55.20	528.35	Yes
19	4183-MYFRB	Female	0	No	No	21	Yes	No	Fiber optic	No	Yes	Yes	No	No	Yes	Month-to-month	Yes	Electronic check	90.05	1862.90	No
20	8779-QRDMV	Male	1	No	No	1	No	No phone service	DSL	No	No	Yes	No	No	Yes	Month-to-month	Yes	Electronic check	39.65	39.65	Yes
21	1680-VDCWW	Male	0	Yes	No	12	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	One year	No	Bank transfer (automatic)	19.80	202.25	No
22	1066-JKSGK	Male	0	No	No	1	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Month-to-month	No	Mailed check	20.15	20.15	Yes
23	3638-WEABW	Female	0	Yes	No	58	Yes	Yes	DSL	No	Yes	No	Yes	No	No	Two year	Yes	Credit card (automatic)	59.90	3505.10	No
24	6322-HRPFA	Male	0	Yes	Yes	49	Yes	No	DSL	Yes	Yes	No	Yes	No	No	Month-to-month	No	Credit card (automatic)	59.60	2970.30	No
25	6865-JZNKO	Female	0	No	No	30	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Bank transfer (automatic)	55.30	1530.60	No
26	6467-CHFZW	Male	0	Yes	Yes	47	Yes	Yes	Fiber optic	No	Yes	No	No	Yes	Yes	Month-to-month	Yes	Electronic check	99.35	4749.15	Yes
27	8665-UTDHZ	Male	0	Yes	Yes	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	No	Electronic check	30.20	30.20	Yes
28	5248-YGIJN	Male	0	Yes	No	72	Yes	Yes	DSL	Yes	Yes	Yes	Yes	Yes	Yes	Two year	Yes	Credit card (automatic)	90.25	6369.45	No
29	8773-HHUOZ	Female	0	No	Yes	17	Yes	No	DSL	No	No	No	No	Yes	Yes	Month-to-month	Yes	Mailed check	64.70	1093.10	Yes
30	3841-NFECX	Female	1	Yes	No	71	Yes	Yes	Fiber optic	Yes	Yes	Yes	Yes	No	No	Two year	Yes	Credit card (automatic)	96.35	6766.95	No
31	4929-XIHVW	Male	1	Yes	No	2	Yes	No	Fiber optic	No	No	Yes	No	Yes	Yes	Month-to-month	Yes	Credit card (automatic)	95.50	181.65	No
32	6827-IEAUQ	Female	0	Yes	Yes	27	Yes	No	DSL	Yes	Yes	Yes	Yes	No	No	One year	No	Mailed check	66.15	1874.45	No
33	7310-EGVHZ	Male	0	No	No	1	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Month-to-month	No	Bank transfer (automatic)	20.20	20.20	No
34	3413-BMNZE	Male	1	No	No	1	Yes	No	DSL	No	No	No	No	No	No	Month-to-month	No	Bank transfer (automatic)	45.25	45.25	No
35	6234-RAAPL	Female	0	Yes	Yes	72	Yes	Yes	Fiber optic	Yes	Yes	No	Yes	Yes	No	Two year	No	Bank transfer (automatic)	99.90	7251.70	No
36	6047-YHPVI	Male	0	No	No	5	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	69.70	316.90	Yes
37	6572-ADKRS	Female	0	No	No	46	Yes	No	Fiber optic	No	No	Yes	No	No	No	Month-to-month	Yes	Credit card (automatic)	74.80	3548.30	No
38	5380-WJKOV	Male	0	No	No	34	Yes	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes	Month-to-month	Yes	Electronic check	106.35	3549.25	Yes
39	8168-UQWWF	Female	0	No	No	11	Yes	Yes	Fiber optic	No	No	Yes	No	Yes	Yes	Month-to-month	Yes	Bank transfer (automatic)	97.85	1105.40	Yes
40	8865-TNMNX	Male	0	Yes	Yes	10	Yes	No	DSL	No	Yes	No	No	No	No	One year	No	Mailed check	49.55	475.70	No
41	9489-DEDVP	Female	0	Yes	Yes	70	Yes	Yes	DSL	Yes	Yes	No	No	Yes	No	Two year	Yes	Credit card (automatic)	69.20	4872.35	No
42	9867-JCZSP	Female	0	Yes	Yes	17	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	One year	No	Mailed check	20.75	418.25	No
43	4671-VJLCL	Female	0	No	No	63	Yes	Yes	DSL	Yes	Yes	Yes	Yes	Yes	No	Two year	Yes	Credit card (automatic)	79.85	4861.45	No
44	4080-IIARD	Female	0	Yes	No	13	Yes	Yes	DSL	Yes	Yes	No	Yes	Yes	No	Month-to-month	Yes	Electronic check	76.20	981.45	No
45	3714-NTNFO	Female	0	No	No	49	Yes	Yes	Fiber optic	No	No	No	No	No	Yes	Month-to-month	Yes	Electronic check	84.50	3906.70	No
46	5948-UJZLF	Male	0	No	No	2	Yes	No	DSL	No	Yes	No	No	No	No	Month-to-month	No	Mailed check	49.25	97.00	No
47	7760-OYPDY	Female	0	No	No	2	Yes	No	Fiber optic	No	No	No	No	Yes	No	Month-to-month	Yes	Electronic check	80.65	144.15	Yes
48	7639-LIAYI	Male	0	No	No	52	Yes	Yes	DSL	Yes	No	No	Yes	Yes	Yes	Two year	Yes	Credit card (automatic)	79.75	4217.80	No
49	2954-PIBKO	Female	0	Yes	Yes	69	Yes	Yes	DSL	Yes	No	Yes	Yes	No	No	Two year	Yes	Credit card (automatic)	64.15	4254.10	No
50	8012-SOUDQ	Female	1	No	No	43	Yes	Yes	Fiber optic	No	Yes	No	No	Yes	No	Month-to-month	Yes	Electronic check	90.25	3838.75	No
51	9420-LOJKX	Female	0	No	No	15	Yes	No	Fiber optic	Yes	Yes	No	No	Yes	Yes	Month-to-month	Yes	Credit card (automatic)	99.10	1426.40	Yes
52	6575-SUVOI	Female	1	Yes	No	25	Yes	Yes	DSL	Yes	No	No	Yes	Yes	No	Month-to-month	Yes	Credit card (automatic)	69.50	1752.65	No
53	7495-OOKFY	Female	1	Yes	No	8	Yes	Yes	Fiber optic	No	Yes	No	No	No	No	Month-to-month	Yes	Credit card (automatic)	80.65	633.30	Yes
54	4667-QONEA	Female	1	Yes	Yes	60	Yes	No	DSL	Yes	Yes	Yes	Yes	No	Yes	One year	Yes	Credit card (automatic)	74.85	4456.35	No
55	1658-BYGOY	Male	1	No	No	18	Yes	Yes	Fiber optic	No	No	No	No	Yes	Yes	Month-to-month	Yes	Electronic check	95.45	1752.55	Yes
56	8769-KKTPH	Female	0	Yes	Yes	63	Yes	Yes	Fiber optic	Yes	No	No	No	Yes	Yes	One year	Yes	Credit card (automatic)	99.65	6311.20	No
57	5067-XJQFU	Male	1	Yes	Yes	66	Yes	Yes	Fiber optic	No	Yes	Yes	Yes	Yes	Yes	One year	Yes	Electronic check	108.45	7076.35	No
58	3957-SQXML	Female	0	Yes	Yes	34	Yes	Yes	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	No	Credit card (automatic)	24.95	894.30	No
59	5954-BDFSG	Female	0	No	No	72	Yes	Yes	Fiber optic	No	No	Yes	Yes	Yes	Yes	Two year	Yes	Credit card (automatic)	107.50	7853.70	No
60	0434-CSFON	Female	0	Yes	No	47	Yes	Yes	Fiber optic	No	No	Yes	No	Yes	Yes	Month-to-month	Yes	Electronic check	100.50	4707.10	No
61	1215-FIGMP	Male	0	No	No	60	Yes	Yes	Fiber optic	No	Yes	No	No	Yes	No	Month-to-month	Yes	Bank transfer (automatic)	89.90	5450.70	No
62	0526-SXDJP	Male	0	Yes	No	72	No	No phone service	DSL	Yes	Yes	Yes	No	No	No	Two year	No	Bank transfer (automatic)	42.10	2962.00	No
63	0557-ASKVU	Female	0	Yes	Yes	18	Yes	No	DSL	No	No	Yes	Yes	No	No	One year	Yes	Credit card (automatic)	54.40	957.10	No
64	5698-BQJOH	Female	0	No	No	9	Yes	Yes	Fiber optic	No	No	No	No	Yes	Yes	Month-to-month	No	Electronic check	94.40	857.25	Yes
65	5122-CYFXA	Female	0	No	No	3	Yes	No	DSL	No	Yes	No	Yes	Yes	Yes	Month-to-month	Yes	Electronic check	75.30	244.10	No
66	8627-ZYGSZ	Male	0	Yes	No	47	Yes	Yes	Fiber optic	No	Yes	No	No	No	No	One year	Yes	Electronic check	78.90	3650.35	No
67	3410-YOQBQ	Female	0	No	No	31	Yes	No	DSL	No	Yes	Yes	Yes	Yes	Yes	Two year	No	Mailed check	79.20	2497.20	No
68	3170-NMYVV	Female	0	Yes	Yes	50	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	No	Bank transfer (automatic)	20.15	930.90	No
69	7410-OIEDU	Male	0	No	No	10	Yes	No	Fiber optic	Yes	No	Yes	No	No	No	Month-to-month	Yes	Mailed check	79.85	887.35	No
70	2273-QCKXA	Male	0	No	No	1	Yes	No	DSL	No	No	No	Yes	No	No	Month-to-month	No	Mailed check	49.05	49.05	No
71	0731-EBJQB	Female	0	Yes	Yes	52	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	One year	Yes	Electronic check	20.40	1090.65	No
72	1891-QRQSA	Male	1	Yes	Yes	64	Yes	Yes	Fiber optic	Yes	No	Yes	Yes	Yes	Yes	Two year	Yes	Bank transfer (automatic)	111.60	7099.00	No
73	8028-PNXHQ	Male	0	Yes	Yes	62	Yes	Yes	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	Yes	Bank transfer (automatic)	24.25	1424.60	No
74	5630-AHZIL	Female	0	No	Yes	3	Yes	No	DSL	Yes	No	No	Yes	No	Yes	Month-to-month	Yes	Bank transfer (automatic)	64.50	177.40	No
75	2673-CXQEU	Female	1	No	No	56	Yes	Yes	Fiber optic	Yes	Yes	Yes	No	Yes	Yes	One year	No	Electronic check	110.50	6139.50	No
76	6416-JNVRK	Female	0	No	No	46	Yes	No	DSL	No	No	No	No	No	Yes	One year	No	Credit card (automatic)	55.65	2688.85	No
77	5590-ZSKRV	Female	0	Yes	Yes	8	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	No	Mailed check	54.65	482.25	No
78	0191-ZHSKZ	Male	1	No	No	30	Yes	No	DSL	Yes	Yes	No	No	Yes	Yes	Month-to-month	Yes	Electronic check	74.75	2111.30	No
79	3887-PBQAO	Female	0	Yes	Yes	45	Yes	Yes	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	One year	Yes	Credit card (automatic)	25.90	1216.60	No
80	5919-TMRGD	Female	0	No	Yes	1	Yes	No	Fiber optic	No	No	No	No	Yes	No	Month-to-month	Yes	Electronic check	79.35	79.35	Yes
81	8108-UXRQN	Female	0	Yes	Yes	11	No	No phone service	DSL	Yes	No	No	No	Yes	Yes	Month-to-month	No	Electronic check	50.55	565.35	No
82	9191-MYQKX	Female	0	Yes	No	7	Yes	No	Fiber optic	No	No	Yes	No	No	No	Month-to-month	Yes	Bank transfer (automatic)	75.15	496.90	Yes
83	9919-YLNNG	Female	0	No	No	42	Yes	No	Fiber optic	No	Yes	Yes	Yes	Yes	Yes	Month-to-month	Yes	Bank transfer (automatic)	103.80	4327.50	No
84	0318-ZOPWS	Female	0	Yes	No	49	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	Yes	Bank transfer (automatic)	20.15	973.35	No
85	4445-ZJNMU	Male	0	No	No	9	Yes	Yes	Fiber optic	No	Yes	No	No	Yes	Yes	Month-to-month	Yes	Credit card (automatic)	99.30	918.75	No
86	4808-YNLEU	Female	0	Yes	No	35	Yes	No	DSL	Yes	No	No	No	Yes	No	One year	Yes	Bank transfer (automatic)	62.15	2215.45	No
87	1862-QRWPE	Female	0	Yes	Yes	48	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	No	Bank transfer (automatic)	20.65	1057.00	No
88	2796-NNUFI	Female	0	Yes	Yes	46	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	Yes	Mailed check	19.95	927.10	No
89	3016-KSVCP	Male	0	Yes	No	29	No	No phone service	DSL	No	No	No	No	Yes	No	Month-to-month	No	Mailed check	33.75	1009.25	No
90	4767-HZZHQ	Male	0	Yes	Yes	30	Yes	No	Fiber optic	No	Yes	Yes	No	No	No	Month-to-month	No	Bank transfer (automatic)	82.05	2570.20	No
91	2424-WVHPL	Male	1	No	No	1	Yes	No	Fiber optic	No	No	No	Yes	No	No	Month-to-month	No	Electronic check	74.70	74.70	No
92	7233-PAHHL	Male	0	Yes	Yes	66	Yes	Yes	DSL	Yes	No	Yes	Yes	Yes	Yes	Two year	Yes	Mailed check	84.00	5714.25	No
93	6067-NGCEU	Female	0	No	No	65	Yes	Yes	Fiber optic	Yes	Yes	Yes	No	Yes	Yes	Month-to-month	Yes	Credit card (automatic)	111.05	7107.00	No
94	9848-JQJTX	Male	0	No	No	72	Yes	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes	Two year	Yes	Bank transfer (automatic)	100.90	7459.05	No
95	8637-XJIVR	Female	0	No	No	12	Yes	Yes	Fiber optic	Yes	No	No	No	No	No	Month-to-month	Yes	Electronic check	78.95	927.35	Yes
96	9803-FTJCG	Male	0	Yes	Yes	71	Yes	Yes	DSL	Yes	Yes	No	Yes	No	No	One year	Yes	Credit card (automatic)	66.85	4748.70	No
97	0278-YXOOG	Male	0	No	No	5	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Month-to-month	No	Mailed check	21.05	113.85	Yes
98	3212-KXOCR	Male	0	No	No	52	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	No	Bank transfer (automatic)	21.00	1107.20	No
99	4598-XLKNJ	Female	1	Yes	No	25	Yes	No	Fiber optic	No	Yes	Yes	No	Yes	Yes	Month-to-month	Yes	Electronic check	98.50	2514.50	Yes

컬럼(특성) 도메인 지식

customID : 사용자 ID (ID의 경우 데이터분석에서 중요하게 다루지 않는다. 추후 드랍할 것이다.)
gender : 성별
SeniorCitizen : 고령자 (SeniorCitizen일 경우 '1' , 아니면 '0' )
Partner : 배우자
Dependents : 자녀
tenure : 얼마나 사용했는지
PhoneService : 핸드폰 서비스
MultipleLines : 전화기 수가 2대 이상 일 경우
InternetService : 인터넷 서비스
OnlineSecurity ~ StreamingMovies : 통신사의 별도 서비스 이용 여부
Contract : 년 단위 계약, 월 단위 계약 인지
PaperlessBilling : 전자영수증 이용 여부
PaymentMethod : 결제 수단
MonthlyCharge : 월별 평균 요금 값
TotalCharges : 요금 총합
Churn : 고객이 이탈을 했는지, 안했는지

In [114]:

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   object 
 20  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.1+ MB

이번 데이터는 텍스트로 된 문자데이터가 많다. 숫자 데이터가 별로 없음을 알 수 있다.

TotalCharges 을 데이터프레임으로 확인했을때는 숫자데이터 였는데, .info()로 실행결과는 object로 나와 있다.
- 그래서 강제로 바꾸어 주는 명령어를 실행하자.

In [115]:

pd.to_numeric(data['TotalCharges'])    # ()안에 바꾸고자하는 컬럼을 넣어주자.

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
pandas/_libs/lib.pyx in pandas._libs.lib.maybe_convert_numeric()

ValueError: Unable to parse string " "

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
<ipython-input-115-742d52be8376> in <module>
----> 1 pd.to_numeric(data['TotalCharges'])    # ()안에 바꾸고자하는 컬럼을 넣어주자.

~/.local/lib/python3.6/site-packages/pandas/core/tools/numeric.py in to_numeric(arg, errors, downcast)
    151         try:
    152             values = lib.maybe_convert_numeric(
--> 153                 values, set(), coerce_numeric=coerce_numeric
    154             )
    155         except (ValueError, TypeError):

pandas/_libs/lib.pyx in pandas._libs.lib.maybe_convert_numeric()

ValueError: Unable to parse string " " at position 488

에러가 나타났는데, 이 에러코드 내용은 488번 포지션에서 " " 공백이 있어서 에러가 나타났다는 뜻이다. 이러한 에러도 해결해 보자.

In [116]:

data.iloc[488]    # row(열)을 인덱싱해서 시리즈 형태로 살펴보자. 488

Out[116]:

customerID                         4472-LVYGI
gender                                 Female
SeniorCitizen                               0
Partner                                   Yes
Dependents                                Yes
tenure                                      0
PhoneService                               No
MultipleLines                No phone service
InternetService                           DSL
OnlineSecurity                            Yes
OnlineBackup                               No
DeviceProtection                          Yes
TechSupport                               Yes
StreamingTV                               Yes
StreamingMovies                            No
Contract                             Two year
PaperlessBilling                          Yes
PaymentMethod       Bank transfer (automatic)
MonthlyCharges                          52.55
TotalCharges                                 
Churn                                      No
Name: 488, dtype: object

TotalCharges 에서 " " 공백이 있으므로, 이를 바꿔주자. .replace() 함수 사용

In [117]:

data['TotalCharges'] = data['TotalCharges'].replace(" ","")           #  " ",을 "" 공백없게 바꾸어주겠다.

In [118]:

data['TotalCharges']

Out[118]:

0         29.85
1       1889.50
2        108.15
3       1840.75
4        151.65
         ...   
7038    1990.50
7039    7362.90
7040     346.45
7041     306.60
7042    6844.50
Name: TotalCharges, Length: 7043, dtype: object

488이 확인이 되지 않지만, 잘 실행 되었기에 굳이 확인할 필요는 없다.

그 후에 pd.to_numeric(data['TotalCharges']) 위에 오류가 났었던, 코드를 다시 실행해보자.

In [119]:

data['TotalCharges'] = pd.to_numeric(data['TotalCharges']) 

In [120]:

data['TotalCharges']

Out[120]:

0         29.85
1       1889.50
2        108.15
3       1840.75
4        151.65
         ...   
7038    1990.50
7039    7362.90
7040     346.45
7041     306.60
7042    6844.50
Name: TotalCharges, Length: 7043, dtype: float64

object에서 float64로 변경된 것을 확인 할 수 있다.

In [121]:

 data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7032 non-null   float64
 20  Churn             7043 non-null   object 
dtypes: float64(2), int64(2), object(17)
memory usage: 1.1+ MB

최종적으로 TotalCharges이 숫자데이터로 변경된 것을 확인했다. (object 에서 float64로)

In [122]:

data.describe()

Out[122]:

	SeniorCitizen	tenure	MonthlyCharges	TotalCharges
count	7043.000000	7043.000000	7043.000000	7032.000000
mean	0.162147	32.371149	64.761692	2283.300441
std	0.368612	24.559481	30.090047	2266.771362
min	0.000000	0.000000	18.250000	18.800000
25%	0.000000	9.000000	35.500000	401.450000
50%	0.000000	29.000000	70.350000	1397.475000
75%	0.000000	55.000000	89.850000	3794.737500
max	1.000000	72.000000	118.750000	8684.800000

In [123]:

sns.distplot(data['TotalCharges'])

/home/ubuntu/.local/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

Out[123]:

<AxesSubplot:xlabel='TotalCharges', ylabel='Density'>

이제는 'gender' 컬럼을 분석에 용이하게 바꿔보자. '0' , '1' 로 바꾸어 주자.

In [124]:

data['gender'].nunique()

Out[124]:

두개의 유니크한 요소가 있다는 것을 확인했다. 우리는 이미 알고 있지만 ( 남자, 여자 )

다음의 코드를 사용해서 바꾸어주자.

In [125]:

pd.get_dummies(data, columns=['gender'], drop_first = True)

Out[125]:

	customerID	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn	gender_Male
0	7590-VHVEG	0	Yes	No	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No	0
1	5575-GNVDE	0	No	No	34	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.50	No	1
2	3668-QPYBK	0	No	No	2	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes	1
3	7795-CFOCW	0	No	No	45	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No	1
4	9237-HQITU	0	No	No	2	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	6840-RESVB	0	Yes	Yes	24	Yes	Yes	DSL	Yes	No	Yes	Yes	Yes	Yes	One year	Yes	Mailed check	84.80	1990.50	No	1
7039	2234-XADUH	0	Yes	Yes	72	Yes	Yes	Fiber optic	No	Yes	Yes	No	Yes	Yes	One year	Yes	Credit card (automatic)	103.20	7362.90	No	0
7040	4801-JZAZL	0	Yes	Yes	11	No	No phone service	DSL	Yes	No	No	No	No	No	Month-to-month	Yes	Electronic check	29.60	346.45	No	0
7041	8361-LTMKD	1	Yes	No	4	Yes	Yes	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Mailed check	74.40	306.60	Yes	1
7042	3186-AJIEK	0	No	No	66	Yes	No	Fiber optic	Yes	No	Yes	Yes	Yes	Yes	Two year	Yes	Bank transfer (automatic)	105.65	6844.50	No	1

7043 rows × 21 columns

In [126]:

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7032 non-null   float64
 20  Churn             7043 non-null   object 
dtypes: float64(2), int64(2), object(17)
memory usage: 1.1+ MB

위에 방법으로 object 을 float로 바꾸고 싶은데, 컬럼수가 너무 많다. 그래서 for문을 통해 처리하려 한다.

In [127]:

data['gender'].dtype

Out[127]:

dtype('O')

In [128]:

data['gender'].dtype == 'O'

Out[128]:

True

In [129]:

col_list = []

In [130]:

data.columns

Out[130]:

Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',
       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',
       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',
       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',
       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],
      dtype='object')

In [131]:

for i in data.columns:
    if data[i].dtype == 'O':
        col_list.append(i)

In [132]:

col_list

Out[132]:

['customerID',
 'gender',
 'Partner',
 'Dependents',
 'PhoneService',
 'MultipleLines',
 'InternetService',
 'OnlineSecurity',
 'OnlineBackup',
 'DeviceProtection',
 'TechSupport',
 'StreamingTV',
 'StreamingMovies',
 'Contract',
 'PaperlessBilling',
 'PaymentMethod',
 'Churn']

In [133]:

for i in col_list:
    print(i, data[i].nunique())

customerID 7043
gender 2
Partner 2
Dependents 2
PhoneService 2
MultipleLines 3
InternetService 3
OnlineSecurity 3
OnlineBackup 3
DeviceProtection 3
TechSupport 3
StreamingTV 3
StreamingMovies 3
Contract 3
PaperlessBilling 2
PaymentMethod 4
Churn 2

customID 은 드랍시켜준다.

In [134]:

col_list = col_list[1:]

In [135]:

col_list

Out[135]:

['gender',
 'Partner',
 'Dependents',
 'PhoneService',
 'MultipleLines',
 'InternetService',
 'OnlineSecurity',
 'OnlineBackup',
 'DeviceProtection',
 'TechSupport',
 'StreamingTV',
 'StreamingMovies',
 'Contract',
 'PaperlessBilling',
 'PaymentMethod',
 'Churn']

사전 작업을 마무리 했다. 위에 썻던 코드를 다시 한번 불러와서 columns을 col_list 로 대체해주자.

In [136]:

data = pd.get_dummies(data, columns=col_list, drop_first = True)

In [137]:

data

Out[137]:

	customerID	SeniorCitizen	tenure	MonthlyCharges	TotalCharges	gender_Male	Partner_Yes	Dependents_Yes	PhoneService_Yes	MultipleLines_No phone service	MultipleLines_Yes	InternetService_Fiber optic	InternetService_No	OnlineSecurity_No internet service	OnlineSecurity_Yes	...	DeviceProtection_No internet service	DeviceProtection_Yes	TechSupport_No internet service	TechSupport_Yes	StreamingTV_No internet service	StreamingTV_Yes	StreamingMovies_No internet service	StreamingMovies_Yes	Contract_One year	Contract_Two year	PaperlessBilling_Yes	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check	Churn_Yes
0	7590-VHVEG	0	1	29.85	29.85	0	1	0	0	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0
1	5575-GNVDE	0	34	56.95	1889.50	1	0	0	1	0	0	0	0	0	1	...	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0
2	3668-QPYBK	0	2	53.85	108.15	1	0	0	1	0	0	0	0	0	1	...	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1
3	7795-CFOCW	0	45	42.30	1840.75	1	0	0	0	1	0	0	0	0	1	...	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0
4	9237-HQITU	0	2	70.70	151.65	0	0	0	1	0	0	1	0	0	0	...	0	0	0	0	0	0	0	0	0	0	1	0	1	0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	6840-RESVB	0	24	84.80	1990.50	1	1	1	1	0	1	0	0	0	1	...	0	1	0	1	0	1	0	1	1	0	1	0	0	1	0
7039	2234-XADUH	0	72	103.20	7362.90	0	1	1	1	0	1	1	0	0	0	...	0	1	0	0	0	1	0	1	1	0	1	1	0	0	0
7040	4801-JZAZL	0	11	29.60	346.45	0	1	1	0	1	0	0	0	0	1	...	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0
7041	8361-LTMKD	1	4	74.40	306.60	1	1	0	1	0	1	1	0	0	0	...	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1
7042	3186-AJIEK	0	66	105.65	6844.50	1	0	0	1	0	0	1	0	0	1	...	0	1	0	1	0	1	0	1	0	1	1	0	0	0	0

7043 rows × 32 columns

컬럼 수는 32개로 늘어났고, '0' , '1' 로 float 데이터로 변환을 성공적으로 했다.

In [138]:

data.isna()

Out[138]:

	customerID	SeniorCitizen	tenure	MonthlyCharges	TotalCharges	gender_Male	Partner_Yes	Dependents_Yes	PhoneService_Yes	MultipleLines_No phone service	MultipleLines_Yes	InternetService_Fiber optic	InternetService_No	OnlineSecurity_No internet service	OnlineSecurity_Yes	...	DeviceProtection_No internet service	DeviceProtection_Yes	TechSupport_No internet service	TechSupport_Yes	StreamingTV_No internet service	StreamingTV_Yes	StreamingMovies_No internet service	StreamingMovies_Yes	Contract_One year	Contract_Two year	PaperlessBilling_Yes	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check	Churn_Yes
0	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
1	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
2	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
3	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
4	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
7039	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
7040	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
7041	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False
7042	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False

7043 rows × 32 columns

In [139]:

data.isna().sum()

Out[139]:

customerID                                0
SeniorCitizen                             0
tenure                                    0
MonthlyCharges                            0
TotalCharges                             11
gender_Male                               0
Partner_Yes                               0
Dependents_Yes                            0
PhoneService_Yes                          0
MultipleLines_No phone service            0
MultipleLines_Yes                         0
InternetService_Fiber optic               0
InternetService_No                        0
OnlineSecurity_No internet service        0
OnlineSecurity_Yes                        0
OnlineBackup_No internet service          0
OnlineBackup_Yes                          0
DeviceProtection_No internet service      0
DeviceProtection_Yes                      0
TechSupport_No internet service           0
TechSupport_Yes                           0
StreamingTV_No internet service           0
StreamingTV_Yes                           0
StreamingMovies_No internet service       0
StreamingMovies_Yes                       0
Contract_One year                         0
Contract_Two year                         0
PaperlessBilling_Yes                      0
PaymentMethod_Credit card (automatic)     0
PaymentMethod_Electronic check            0
PaymentMethod_Mailed check                0
Churn_Yes                                 0
dtype: int64

TotalCharges 컬럼에 11개의 결측치를 확인했다. mean 값으로 넣을지, median 값으로 넣을지 고려해보자. 시각화한 그래프를 보고 결정하자.

In [140]:

data['TotalCharges'].mean()

Out[140]:

2283.3004408418656

In [141]:

data['TotalCharges'].median()

Out[141]:

1397.475

In [142]:

sns.distplot(data['TotalCharges'])

/home/ubuntu/.local/lib/python3.6/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

Out[142]:

<AxesSubplot:xlabel='TotalCharges', ylabel='Density'>

이러한 분포의 데이터라면 median 값을 결측치로 채워주는게 좋은 방법 이다.

In [143]:

data['TotalCharges'] = data['TotalCharges'].fillna(data['TotalCharges'].median())

결측치가 이제는 없는지 확인해 보자.

In [144]:

data.isna().sum()

Out[144]:

customerID                               0
SeniorCitizen                            0
tenure                                   0
MonthlyCharges                           0
TotalCharges                             0
gender_Male                              0
Partner_Yes                              0
Dependents_Yes                           0
PhoneService_Yes                         0
MultipleLines_No phone service           0
MultipleLines_Yes                        0
InternetService_Fiber optic              0
InternetService_No                       0
OnlineSecurity_No internet service       0
OnlineSecurity_Yes                       0
OnlineBackup_No internet service         0
OnlineBackup_Yes                         0
DeviceProtection_No internet service     0
DeviceProtection_Yes                     0
TechSupport_No internet service          0
TechSupport_Yes                          0
StreamingTV_No internet service          0
StreamingTV_Yes                          0
StreamingMovies_No internet service      0
StreamingMovies_Yes                      0
Contract_One year                        0
Contract_Two year                        0
PaperlessBilling_Yes                     0
PaymentMethod_Credit card (automatic)    0
PaymentMethod_Electronic check           0
PaymentMethod_Mailed check               0
Churn_Yes                                0
dtype: int64

전부 0으로, 완전한 데이터가 완성되었다.

스케일링 하는 방법 3가지를 알아보자.¶

StandardScaler, MinMaxScaler, RobustScaler 3가지 방법중 이번 예제 데이터는 MinMaxScaler 을 추천한다. 왜 그런지 살펴보자.

MinMaxScaler

In [145]:

from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler

In [146]:

mixmax = MinMaxScaler()

In [147]:

data.drop('customerID', axis=1, inplace=True)

In [148]:

data.head(3)

Out[148]:

	tenure	MonthlyCharges	TotalCharges	gender_Male	Partner_Yes	PhoneService_Yes	MultipleLines_No phone service	OnlineSecurity_Yes	...	DeviceProtection_Yes	Contract_One year	PaperlessBilling_Yes	PaymentMethod_Electronic check	PaymentMethod_Mailed check	Churn_Yes
0	1	29.85	29.85	0	1	0	1	0	...	0	0	1	1	0	0
1	34	56.95	1889.50	1	0	1	0	1	...	1	1	0	0	1	0
2	2	53.85	108.15	1	0	1	0	1	...	0	0	1	0	1	1

3 rows × 31 columns

In [149]:

mixmax.fit(data)

Out[149]:

MinMaxScaler()

In [150]:

mixmax.transform(data)

Out[150]:

array([[0.        , 0.01388889, 0.11542289, ..., 1.        , 0.        ,
        0.        ],
       [0.        , 0.47222222, 0.38507463, ..., 0.        , 1.        ,
        0.        ],
       [0.        , 0.02777778, 0.35422886, ..., 0.        , 1.        ,
        1.        ],
       ...,
       [0.        , 0.15277778, 0.11293532, ..., 1.        , 0.        ,
        0.        ],
       [1.        , 0.05555556, 0.55870647, ..., 0.        , 1.        ,
        1.        ],
       [0.        , 0.91666667, 0.86965174, ..., 0.        , 0.        ,
        0.        ]])

In [151]:

scaled_data = mixmax.transform(data)

In [153]:

pd.DataFrame(scaled_data)   # 위에 결과값을 데이터프레임으로 보기 위해

Out[153]:

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	...	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30
0	0.0	0.013889	0.115423	0.001275	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0
1	0.0	0.472222	0.385075	0.215867	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0
2	0.0	0.027778	0.354229	0.010310	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0
3	0.0	0.625000	0.239303	0.210241	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0
4	0.0	0.027778	0.521891	0.015330	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	0.0	0.333333	0.662189	0.227521	1.0	1.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	...	0.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0
7039	0.0	1.000000	0.845274	0.847461	0.0	1.0	1.0	1.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	...	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	1.0	0.0	1.0	1.0	0.0	0.0	0.0
7040	0.0	0.152778	0.112935	0.037809	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0
7041	1.0	0.055556	0.558706	0.033210	1.0	1.0	0.0	1.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0
7042	0.0	0.916667	0.869652	0.787641	1.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0

7043 rows × 31 columns

컬럼 값 또한 넘파이 array 값으로 나왔기 때문에, 다음의 코드로 바꿔주자.

In [154]:

data.columns

Out[154]:

Index(['SeniorCitizen', 'tenure', 'MonthlyCharges', 'TotalCharges',
       'gender_Male', 'Partner_Yes', 'Dependents_Yes', 'PhoneService_Yes',
       'MultipleLines_No phone service', 'MultipleLines_Yes',
       'InternetService_Fiber optic', 'InternetService_No',
       'OnlineSecurity_No internet service', 'OnlineSecurity_Yes',
       'OnlineBackup_No internet service', 'OnlineBackup_Yes',
       'DeviceProtection_No internet service', 'DeviceProtection_Yes',
       'TechSupport_No internet service', 'TechSupport_Yes',
       'StreamingTV_No internet service', 'StreamingTV_Yes',
       'StreamingMovies_No internet service', 'StreamingMovies_Yes',
       'Contract_One year', 'Contract_Two year', 'PaperlessBilling_Yes',
       'PaymentMethod_Credit card (automatic)',
       'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check',
       'Churn_Yes'],
      dtype='object')

In [155]:

pd.DataFrame(scaled_data, columns=data.columns)  # 컬럼 바꿔주기

Out[155]:

	SeniorCitizen	tenure	MonthlyCharges	TotalCharges	gender_Male	Partner_Yes	Dependents_Yes	PhoneService_Yes	MultipleLines_No phone service	MultipleLines_Yes	InternetService_Fiber optic	InternetService_No	OnlineSecurity_No internet service	OnlineSecurity_Yes	OnlineBackup_No internet service	...	DeviceProtection_No internet service	DeviceProtection_Yes	TechSupport_No internet service	TechSupport_Yes	StreamingTV_No internet service	StreamingTV_Yes	StreamingMovies_No internet service	StreamingMovies_Yes	Contract_One year	Contract_Two year	PaperlessBilling_Yes	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check	Churn_Yes
0	0.0	0.013889	0.115423	0.001275	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0
1	0.0	0.472222	0.385075	0.215867	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0
2	0.0	0.027778	0.354229	0.010310	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0
3	0.0	0.625000	0.239303	0.210241	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0
4	0.0	0.027778	0.521891	0.015330	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	0.0	0.333333	0.662189	0.227521	1.0	1.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	...	0.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0
7039	0.0	1.000000	0.845274	0.847461	0.0	1.0	1.0	1.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	...	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	1.0	0.0	1.0	1.0	0.0	0.0	0.0
7040	0.0	0.152778	0.112935	0.037809	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0
7041	1.0	0.055556	0.558706	0.033210	1.0	1.0	0.0	1.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0
7042	0.0	0.916667	0.869652	0.787641	1.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0

7043 rows × 31 columns

In [157]:

data

Out[157]:

	SeniorCitizen	tenure	MonthlyCharges	TotalCharges	gender_Male	Partner_Yes	Dependents_Yes	PhoneService_Yes	MultipleLines_No phone service	MultipleLines_Yes	InternetService_Fiber optic	InternetService_No	OnlineSecurity_No internet service	OnlineSecurity_Yes	OnlineBackup_No internet service	...	DeviceProtection_No internet service	DeviceProtection_Yes	TechSupport_No internet service	TechSupport_Yes	StreamingTV_No internet service	StreamingTV_Yes	StreamingMovies_No internet service	StreamingMovies_Yes	Contract_One year	Contract_Two year	PaperlessBilling_Yes	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check	Churn_Yes
0	0	1	29.85	29.85	0	1	0	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0
1	0	34	56.95	1889.50	1	0	0	1	0	0	0	0	0	1	0	...	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0
2	0	2	53.85	108.15	1	0	0	1	0	0	0	0	0	1	0	...	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1
3	0	45	42.30	1840.75	1	0	0	0	1	0	0	0	0	1	0	...	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0
4	0	2	70.70	151.65	0	0	0	1	0	0	1	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	1	0	1	0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	0	24	84.80	1990.50	1	1	1	1	0	1	0	0	0	1	0	...	0	1	0	1	0	1	0	1	1	0	1	0	0	1	0
7039	0	72	103.20	7362.90	0	1	1	1	0	1	1	0	0	0	0	...	0	1	0	0	0	1	0	1	1	0	1	1	0	0	0
7040	0	11	29.60	346.45	0	1	1	0	1	0	0	0	0	1	0	...	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0
7041	1	4	74.40	306.60	1	1	0	1	0	1	1	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1
7042	0	66	105.65	6844.50	1	0	0	1	0	0	1	0	0	1	0	...	0	1	0	1	0	1	0	1	0	1	1	0	0	0	0

7043 rows × 31 columns

기존 data와 비교해보자. 스케일링 하기전.

- tenure 컬럼을 예로들어 34라는 수치는 대략 47%정도 비중임을 알 수 있다.

StandardScaler

종속변수 드랍하고 진행

In [173]:

standard = StandardScaler()

In [174]:

standard.fit(data.drop('Churn_Yes', axis=1))

Out[174]:

StandardScaler()

In [175]:

scaled_st = standard.transform(data.drop('Churn_Yes', axis=1))

In [177]:

pd.DataFrame(scaled_st, columns = data.drop('Churn_Yes', axis=1).columns)

Out[177]:

	SeniorCitizen	tenure	MonthlyCharges	TotalCharges	gender_Male	Partner_Yes	Dependents_Yes	PhoneService_Yes	MultipleLines_No phone service	MultipleLines_Yes	InternetService_Fiber optic	InternetService_No	OnlineSecurity_No internet service	OnlineSecurity_Yes	OnlineBackup_No internet service	OnlineBackup_Yes	DeviceProtection_No internet service	DeviceProtection_Yes	TechSupport_No internet service	TechSupport_Yes	StreamingTV_No internet service	StreamingTV_Yes	StreamingMovies_No internet service	StreamingMovies_Yes	Contract_One year	Contract_Two year	PaperlessBilling_Yes	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check
0	-0.439916	-1.277445	-1.160323	-0.994242	-1.009559	1.034530	-0.654012	-3.054010	3.054010	-0.854176	-0.885660	-0.525927	-0.525927	-0.633933	-0.525927	1.378241	-0.525927	-0.723968	-0.525927	-0.639439	-0.525927	-0.790132	-0.525927	-0.796070	-0.514249	-0.562975	0.829798	-0.525047	1.406418	-0.544807
1	-0.439916	0.066327	-0.259629	-0.173244	0.990532	-0.966622	-0.654012	0.327438	-0.327438	-0.854176	-0.885660	-0.525927	-0.525927	1.577454	-0.525927	-0.725563	-0.525927	1.381277	-0.525927	-0.639439	-0.525927	-0.790132	-0.525927	-0.796070	1.944582	-0.562975	-1.205113	-0.525047	-0.711026	1.835513
2	-0.439916	-1.236724	-0.362660	-0.959674	0.990532	-0.966622	-0.654012	0.327438	-0.327438	-0.854176	-0.885660	-0.525927	-0.525927	1.577454	-0.525927	1.378241	-0.525927	-0.723968	-0.525927	-0.639439	-0.525927	-0.790132	-0.525927	-0.796070	-0.514249	-0.562975	0.829798	-0.525047	-0.711026	1.835513
3	-0.439916	0.514251	-0.746535	-0.194766	0.990532	-0.966622	-0.654012	-3.054010	3.054010	-0.854176	-0.885660	-0.525927	-0.525927	1.577454	-0.525927	-0.725563	-0.525927	1.381277	-0.525927	1.563872	-0.525927	-0.790132	-0.525927	-0.796070	1.944582	-0.562975	-1.205113	-0.525047	-0.711026	-0.544807
4	-0.439916	-1.236724	0.197365	-0.940470	-1.009559	-0.966622	-0.654012	0.327438	-0.327438	-0.854176	1.129102	-0.525927	-0.525927	-0.633933	-0.525927	-0.725563	-0.525927	-0.723968	-0.525927	-0.639439	-0.525927	-0.790132	-0.525927	-0.796070	-0.514249	-0.562975	0.829798	-0.525047	1.406418	-0.544807
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	-0.439916	-0.340876	0.665992	-0.128655	0.990532	1.034530	1.529024	0.327438	-0.327438	1.170719	-0.885660	-0.525927	-0.525927	1.577454	-0.525927	-0.725563	-0.525927	1.381277	-0.525927	1.563872	-0.525927	1.265612	-0.525927	1.256171	1.944582	-0.562975	0.829798	-0.525047	-0.711026	1.835513
7039	-0.439916	1.613701	1.277533	2.243151	-1.009559	1.034530	1.529024	0.327438	-0.327438	1.170719	1.129102	-0.525927	-0.525927	-0.633933	-0.525927	1.378241	-0.525927	1.381277	-0.525927	-0.639439	-0.525927	1.265612	-0.525927	1.256171	1.944582	-0.562975	0.829798	1.904590	-0.711026	-0.544807
7040	-0.439916	-0.870241	-1.168632	-0.854469	-1.009559	1.034530	1.529024	-3.054010	3.054010	-0.854176	-0.885660	-0.525927	-0.525927	1.577454	-0.525927	-0.725563	-0.525927	-0.723968	-0.525927	-0.639439	-0.525927	-0.790132	-0.525927	-0.796070	-0.514249	-0.562975	0.829798	-0.525047	1.406418	-0.544807
7041	2.273159	-1.155283	0.320338	-0.872062	0.990532	1.034530	-0.654012	0.327438	-0.327438	1.170719	1.129102	-0.525927	-0.525927	-0.633933	-0.525927	-0.725563	-0.525927	-0.723968	-0.525927	-0.639439	-0.525927	-0.790132	-0.525927	-0.796070	-0.514249	-0.562975	0.829798	-0.525047	-0.711026	1.835513
7042	-0.439916	1.369379	1.358961	2.014288	0.990532	-0.966622	-0.654012	0.327438	-0.327438	-0.854176	1.129102	-0.525927	-0.525927	1.577454	-0.525927	-0.725563	-0.525927	1.381277	-0.525927	1.563872	-0.525927	1.265612	-0.525927	1.256171	-0.514249	1.776278	0.829798	-0.525047	-0.711026	-0.544807

7043 rows × 30 columns

RobustScaler

종속변수 드랍하고 진행

In [178]:

rob = RobustScaler()

In [179]:

rob.fit(data.drop('Churn_Yes', axis=1))

Out[179]:

RobustScaler()

In [181]:

scaled_rob = rob.transform(data.drop('Churn_Yes', axis=1))

In [187]:

pd.DataFrame(scaled_rob, columns= data.drop('Churn_Yes', axis=1).columns)

Out[187]:

	SeniorCitizen	tenure	MonthlyCharges	TotalCharges	gender_Male	Partner_Yes	Dependents_Yes	PhoneService_Yes	MultipleLines_No phone service	MultipleLines_Yes	InternetService_Fiber optic	InternetService_No	OnlineSecurity_No internet service	OnlineSecurity_Yes	OnlineBackup_No internet service	OnlineBackup_Yes	DeviceProtection_No internet service	DeviceProtection_Yes	TechSupport_No internet service	TechSupport_Yes	StreamingTV_No internet service	StreamingTV_Yes	StreamingMovies_No internet service	StreamingMovies_Yes	Contract_One year	Contract_Two year	PaperlessBilling_Yes	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check
0	0.0	-0.608696	-0.745170	-0.404100	-1.0	1.0	0.0	-1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
1	0.0	0.108696	-0.246550	0.145381	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	-1.0	0.0	0.0	1.0
2	0.0	-0.586957	-0.303588	-0.380964	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
3	0.0	0.347826	-0.516099	0.130977	0.0	0.0	0.0	-1.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	-1.0	0.0	0.0	0.0
4	0.0	-0.586957	0.006440	-0.368111	-1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	0.0	-0.108696	0.265869	0.175224	0.0	1.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0
7039	0.0	0.934783	0.604416	1.762637	-1.0	1.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	1.0	0.0	0.0	1.0	0.0	0.0
7040	0.0	-0.391304	-0.749770	-0.310552	-1.0	1.0	1.0	-1.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
7041	1.0	-0.543478	0.074517	-0.322327	0.0	1.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
7042	0.0	0.804348	0.649494	1.609463	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0

7043 rows × 30 columns

이 데이터의 경우 MinMaxScaler 을 사용한다. 나머지는 정의를 하지 않고, MinMaxScaler 를 scaled_data 로 정의해주겠다.

In [188]:

scaled_data = pd.DataFrame(scaled_data, columns=data.columns)

그리고 train_test_split 을 하겠다.

In [190]:

from sklearn.model_selection import train_test_split

In [191]:

scaled_data

Out[191]:

	SeniorCitizen	tenure	MonthlyCharges	TotalCharges	gender_Male	Partner_Yes	Dependents_Yes	PhoneService_Yes	MultipleLines_No phone service	MultipleLines_Yes	InternetService_Fiber optic	InternetService_No	OnlineSecurity_No internet service	OnlineSecurity_Yes	OnlineBackup_No internet service	...	DeviceProtection_No internet service	DeviceProtection_Yes	TechSupport_No internet service	TechSupport_Yes	StreamingTV_No internet service	StreamingTV_Yes	StreamingMovies_No internet service	StreamingMovies_Yes	Contract_One year	Contract_Two year	PaperlessBilling_Yes	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check	Churn_Yes
0	0.0	0.013889	0.115423	0.001275	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0
1	0.0	0.472222	0.385075	0.215867	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0
2	0.0	0.027778	0.354229	0.010310	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0
3	0.0	0.625000	0.239303	0.210241	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0
4	0.0	0.027778	0.521891	0.015330	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7038	0.0	0.333333	0.662189	0.227521	1.0	1.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	...	0.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0	1.0	0.0	1.0	0.0	0.0	1.0	0.0
7039	0.0	1.000000	0.845274	0.847461	0.0	1.0	1.0	1.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	...	0.0	1.0	0.0	0.0	0.0	1.0	0.0	1.0	1.0	0.0	1.0	1.0	0.0	0.0	0.0
7040	0.0	0.152778	0.112935	0.037809	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0
7041	1.0	0.055556	0.558706	0.033210	1.0	1.0	0.0	1.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0
7042	0.0	0.916667	0.869652	0.787641	1.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	...	0.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0

7043 rows × 31 columns

In [192]:

X = scaled_data.drop('Churn_Yes', axis = 1 )
y = scaled_data['Churn_Yes']

In [193]:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=100)

KNN 알고리즘을 불러온다.

In [197]:

from sklearn.neighbors import KNeighborsClassifier

In [201]:

knn = KNeighborsClassifier(n_neighbors=10)

In [202]:

knn.fit(X_train, y_train)

Out[202]:

KNeighborsClassifier(n_neighbors=10)

In [203]:

knn.predict(X_test)

Out[203]:

array([0., 0., 0., ..., 0., 0., 0.])

In [205]:

pred = knn.predict(X_test)

In [206]:

pd.DataFrame(pred, y_test)

Out[206]:

	0
Churn_Yes
0.0	0.0
0.0	0.0
0.0	0.0
0.0	0.0
0.0	0.0
...	...
1.0	1.0
0.0	0.0
0.0	0.0
0.0	0.0
0.0	0.0

2113 rows × 1 columns

여기서 딕셔너리를 사용해서 바꿔준다. 보기 좋게

In [207]:

pd.DataFrame({'acutal_value': y_test, 'pred_value': pred})

Out[207]:

	acutal_value	pred_value
4880	0.0	0.0
1541	0.0	0.0
1289	0.0	0.0
5745	0.0	0.0
4873	0.0	0.0
...	...	...
1285	1.0	1.0
5092	0.0	0.0
5837	0.0	0.0
3597	0.0	0.0
3625	0.0	0.0

2113 rows × 2 columns

In [208]:

pd.DataFrame({'acutal_value': y_test, 'pred_value': pred}).head(30)

Out[208]:

	acutal_value	pred_value
4880	0.0	0.0
1541	0.0	0.0
1289	0.0	0.0
5745	0.0	0.0
4873	0.0	0.0
4168	0.0	0.0
1557	0.0	0.0
2892	0.0	0.0
664	0.0	0.0
1588	0.0	0.0
1338	1.0	0.0
6000	0.0	0.0
2310	0.0	0.0
3294	1.0	1.0
290	1.0	1.0
2505	0.0	0.0
3171	0.0	0.0
1366	1.0	1.0
6560	0.0	0.0
2420	0.0	0.0
5210	1.0	0.0
2836	0.0	1.0
1325	1.0	1.0
4900	1.0	0.0
6311	0.0	0.0
1025	0.0	0.0
2031	0.0	0.0
4459	1.0	1.0
5324	0.0	0.0
3441	0.0	0.0

최적의 n_neighbors 찾기

In [209]:

from sklearn.metrics import accuracy_score, confusion_matrix

In [214]:

accuracy_score(y_test, pred)

Out[214]:

0.7581637482252721

In [215]:

confusion_matrix(y_test, pred)

Out[215]:

array([[1353,  194],
       [ 317,  249]])

for 문을 통해 n_neighbors = 1 ~ 100 대입해보기

In [217]:

error_list = []

In [219]:

for i in range(1,101):
    knn = KNeighborsClassifier(n_neighbors = i )
    knn.fit(X_train, y_train)
    pred = knn.predict(X_test)
    error_list.append(accuracy_score(y_test, pred))

In [221]:

error_list

Out[221]:

[0.7136772361571225,
 0.7491717936583058,
 0.7439659252247989,
 0.7557974443918599,
 0.7482252721249408,
 0.7581637482252721,
 0.7458589682915286,
 0.759110269758637,
 0.7529578797917653,
 0.7581637482252721,
 0.7624230951254141,
 0.7610033128253668,
 0.7600567912920019,
 0.7671557027922385,
 0.7662091812588736,
 0.7681022243256034,
 0.7690487458589683,
 0.7728348319924279,
 0.7681022243256034,
 0.7723615712257453,
 0.7681022243256034,
 0.7714150496923805,
 0.7699952673923331,
 0.7652626597255088,
 0.767628963558921,
 0.7714150496923805,
 0.7733080927591103,
 0.7728348319924279,
 0.7733080927591103,
 0.7714150496923805,
 0.7699952673923331,
 0.767628963558921,
 0.7709417889256981,
 0.7704685281590156,
 0.7742546142924751,
 0.7737813535257927,
 0.7733080927591103,
 0.77520113582584,
 0.7718883104590629,
 0.7737813535257927,
 0.7723615712257453,
 0.7775674396592522,
 0.7766209181258874,
 0.7747278750591576,
 0.7756743965925225,
 0.7766209181258874,
 0.7775674396592522,
 0.7775674396592522,
 0.7742546142924751,
 0.7709417889256981,
 0.7714150496923805,
 0.7756743965925225,
 0.77520113582584,
 0.780407004259347,
 0.7742546142924751,
 0.7770941788925698,
 0.7761476573592049,
 0.77520113582584,
 0.7747278750591576,
 0.7699952673923331,
 0.7704685281590156,
 0.7695220066256507,
 0.7709417889256981,
 0.7671557027922385,
 0.7690487458589683,
 0.7681022243256034,
 0.7709417889256981,
 0.7728348319924279,
 0.7709417889256981,
 0.7695220066256507,
 0.7742546142924751,
 0.7714150496923805,
 0.7709417889256981,
 0.7718883104590629,
 0.7690487458589683,
 0.7723615712257453,
 0.7718883104590629,
 0.7742546142924751,
 0.7718883104590629,
 0.7723615712257453,
 0.7733080927591103,
 0.7690487458589683,
 0.7709417889256981,
 0.7714150496923805,
 0.7685754850922859,
 0.7652626597255088,
 0.7695220066256507,
 0.7685754850922859,
 0.7681022243256034,
 0.7709417889256981,
 0.7690487458589683,
 0.7699952673923331,
 0.7685754850922859,
 0.7718883104590629,
 0.7666824420255561,
 0.7690487458589683,
 0.767628963558921,
 0.7695220066256507,
 0.7681022243256034,
 0.7690487458589683]

In [230]:

plt.figure(figsize=(20,10))
sns.lineplot(x = range(1,101), y = error_list, marker = 'o', markersize = 8 , markerfacecolor = 'red')

Out[230]:

<AxesSubplot:>

y축의 값 즉, n_neighbors 을 몇으로 할때 가장 높은 값이 나왔나? 그래프상으론 대략 어느 지점이다라는 건은 알 수 있다. 코딩으로 그 지점을 찾는 방법을 알아보자.

첫번째 방법

In [232]:

max(error_list)

Out[232]:

0.780407004259347

In [233]:

error_list.index(max(error_list))

Out[233]:

n_neighbors = 54 을 쓸때, 0.780407004259347 로 가장 높은 수치가 나왔다. 0 부터 시작 하기 때문에 53이 아닌 54 이다.

두번째 방법 : 넘파이 형태로 바꿔서 하기 (기능이 있음)

In [236]:

np.array(error_list)   # 넘파이 형태로 바꾸기

Out[236]:

array([0.71367724, 0.74917179, 0.74396593, 0.75579744, 0.74822527,
       0.75816375, 0.74585897, 0.75911027, 0.75295788, 0.75816375,
       0.7624231 , 0.76100331, 0.76005679, 0.7671557 , 0.76620918,
       0.76810222, 0.76904875, 0.77283483, 0.76810222, 0.77236157,
       0.76810222, 0.77141505, 0.76999527, 0.76526266, 0.76762896,
       0.77141505, 0.77330809, 0.77283483, 0.77330809, 0.77141505,
       0.76999527, 0.76762896, 0.77094179, 0.77046853, 0.77425461,
       0.77378135, 0.77330809, 0.77520114, 0.77188831, 0.77378135,
       0.77236157, 0.77756744, 0.77662092, 0.77472788, 0.7756744 ,
       0.77662092, 0.77756744, 0.77756744, 0.77425461, 0.77094179,
       0.77141505, 0.7756744 , 0.77520114, 0.780407  , 0.77425461,
       0.77709418, 0.77614766, 0.77520114, 0.77472788, 0.76999527,
       0.77046853, 0.76952201, 0.77094179, 0.7671557 , 0.76904875,
       0.76810222, 0.77094179, 0.77283483, 0.77094179, 0.76952201,
       0.77425461, 0.77141505, 0.77094179, 0.77188831, 0.76904875,
       0.77236157, 0.77188831, 0.77425461, 0.77188831, 0.77236157,
       0.77330809, 0.76904875, 0.77094179, 0.77141505, 0.76857549,
       0.76526266, 0.76952201, 0.76857549, 0.76810222, 0.77094179,
       0.76904875, 0.76999527, 0.76857549, 0.77188831, 0.76668244,
       0.76904875, 0.76762896, 0.76952201, 0.76810222, 0.76904875])

In [237]:

np.array(error_list).argmax()

Out[237]:

두가지 방법으로 결국 n_neighbors = 54 일때 가장 정확도가 높다는 것을 알았다. 이 치수로 넣어 다시 결과를 보자.

In [238]:

knn = KNeighborsClassifier(n_neighbors=54)

In [239]:

knn.fit(X_train, y_train)

Out[239]:

KNeighborsClassifier(n_neighbors=54)

In [240]:

pred = knn.predict(X_test)

In [241]:

accuracy_score(y_test, pred)

Out[241]:

0.780407004259347

In [243]:

confusion_matrix(y_test, pred)

Out[243]:

array([[1332,  215],
       [ 249,  317]])

n_neighbors을 조절해서 정확도를 조금 높일 수 있었다.

출처:fast campus_파이썬을 활용한 이커머스 데이터 분석

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